The modified model for hyperspectral estimation of soil organic matter using positive and inverse grey relational degree

Abstract

PurposeIn order to improve the estimation accuracy of soil organic matter, this paper aims to establish a modified model for hyperspectral estimation of soil organic matter content based on the positive and inverse grey relational degrees.Design/methodology/approachBased on 82 soil sample data collected in Daiyue District, Tai'an City, Shandong Province, firstly, the spectral data of soil samples are transformed by the first order differential and logarithmic reciprocal first order differential and so on, the correlation coefficients between the transformed spectral data and soil organic matter content are calculated, and the estimation factors are selected according to the principle of maximum correlation. Secondly, the positive and inverse grey relational degree model is used to identify the samples to be identified, and the initial estimated values of the organic matter content are obtained. Finally, based on the difference information between the samples to be identified and their corresponding known patterns, a modified model for the initial estimation of soil organic matter content is established, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.FindingsThe results show that the methods of logarithmic reciprocal first order differential and the first-order differential of the square root for transforming the original spectral data are more effective, which could significantly improve the correlation between soil organic matter content and spectral data. The modified model for hyperspectral estimation of soil organic matter has high estimation accuracy, the average relative error (MRE) of 11 test samples is 4.091%, and the determination coefficient (R2) is 0.936. The estimation precision is higher than that of linear regression model, BP neural network and support vector machine model. The application examples show that the modified model for hyperspectral estimation of soil organic matter content based on positive and inverse grey relational degree proposed in this article is feasible and effective.Social implicationsThe model in this paper has clear mathematical and physics meaning, simple calculation and easy programming. The model not only fully excavates and utilizes the internal information of known pattern samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation of soil organic matter. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on.Originality/valueThe paper succeeds in realizing both a modified model for hyperspectral estimation of soil organic matter based on the positive and inverse grey relational degrees and effectively dealing with the randomness and grey uncertainty in spectral estimation.

Hyperspectral estimation model of soil organic matter content based on principal gradient grey information

“…To make full use of the information of multiple known pattern samples, Zhong et al (2021) established a positive and inverse grey relational estimation model. With the development of research, some new models are proposed, such as the grey relational local regression estimation model (Cao et al, 2020), optimal model of positive and inverse grey relational estimation (Ren et al, 2021), estimation model based on grey number and grey scale (Ding et al, 2022), grey fuzzy estimation model (Yu et al, 2023), correction model based on positive and inverse grey relational degree (Xu et al, 2023) and so on. These research results further enrich the methodology for spectral estimation of soil organic matter content, but they are still the methods based on soil spectral features.…”

“…, 2022), grey fuzzy estimation model (Yu et al. , 2023), correction model based on positive and inverse grey relational degree (Xu et al. , 2023) and so on.…”